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Using Periodicity Intensity to Detect Long Term Behaviour Change

Authors: 

Feiyan Hu, Alan Smeaton, Eamonn Newman, Matthew P. Buman

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
This paper introduces a new way to analyse and visualize quantified-self or lifelog data captured from any lifelogging device over an extended period of time. The mechanism works on the raw, unstructured lifelog data by detecting periodicities, those repeating patters that occur within our lifestyles at different frequencies including daily, weekly, seasonal, etc. Focusing on the 24 hour cycle, we calculate the strength of the 24-hour periodicity at 24-hour intervals over an extended period of a lifelog. Changes in this strength of the 24-hour cycle can illustrate changes or shifts in underlying human behavior. We have performed this analysis on several lifelog datasets of durations from several weeks to almost a decade, from recordings of training distances to sleep data. In this paper we use 24 hour accelerometer data to illustrate the technique, showing how changes in human behavior can be identified.
Conference Name: 
2015 ACM International Joint conference on Pervasive and Ubiquitous Computing (UbiComp 2015)
Proceedings: 
2015 ACM International Joint conference on Pervasive and Ubiquitous Computing (UbiComp 2015)
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
07/09/2015
Conference Location: 
Japan
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes